Abstract The progressive paradigm is a promising strategy to optimize network performance for speech enhancement purposes. Recent works have shown different strategies improve the accuracy of solutions based on this mechanism. This paper studies using convolutional and residual neural architectures explores two criteria loss function optimization: weighted uniform progressive. work carries out ...

We develop new approaches in multi-class settings for constructing loss functions and establishing corresponding regret bounds with respect to the zero-one or cost-weighted classification loss. provide general representations of losses by deriving inverse mappings from a concave generalized entropy through convex dissimilarity function related multi-distribution <inline-formula xmlns:mml="http:...

Recent works have shown that deep neural networks can be employed to solve partial differential equations, giving rise the framework of physics informed (Raissi et al., 2007). We introduce a generalization for these methods manifests as scaling parameter which balances relative importance different constraints imposed by equations. A mathematical motivation generalized is provided, shows linear...